Broken String Biosciences and BioLizard Partner to Develop AI-Powered SafeGuide for CRISPR Gene Editing Safety
核心洞察
Broken String Biosciences (搜索) and BioLizard (搜索) have announced the development of SafeGuide (搜索), an AI tool designed to select safer guide RNAs for CRISPR (搜索) gene editing applications.
The collaboration aims to replace current trial-and-error approaches that take one to two years and result in drug costs exceeding $4 million per patient.
The project is supported by a $935,000 grant from the competitive Eurostars-3 program, part of the European Partnership on Innovative SMEs.
Broken String Biosciences (搜索) and BioLizard (搜索) have announced a strategic partnership to develop SafeGuide (搜索), an artificial intelligence tool designed to accelerate the selection of safer guide RNAs (gRNAs) for CRISPR (搜索) gene editing applications. The collaboration addresses a critical bottleneck in gene therapy development, where current trial-and-error approaches often require one to two years and result in drug costs exceeding $4 million per patient.
The project has secured $935,000 in funding through the highly competitive Eurostars-3 program, part of the European Partnership on Innovative SMEs co-funded by 37 participating countries and the EU's Horizon Europe program. Moore Grants & Incentives (搜索) (MGI) served as a strategic partner, helping shape the project proposal and contributing to its funding success.
Combining Complementary Technologies
SafeGuide (搜索) represents the integration of two distinct technological capabilities. Broken String Biosciences (搜索) will contribute data and expertise from its INDUCE-seq (搜索) platform, which can pinpoint where on- and off-target edits occur both naturally and through gene editing methods like CRISPR (搜索) with high sensitivity. This genomic break data will provide crucial insights to train SafeGuide's AI models to distinguish between safe and high-risk gRNA designs.
BioLizard (搜索) will develop predictive algorithms that integrate Broken String's genomic break data with other sequence and structural features to recommend gRNAs with high on-target efficacy and low off-target risk. The AI tool aims to significantly reduce reliance on time-consuming and expensive wet laboratory testing currently required for gRNA optimization.
Addressing Gene Editing Safety Challenges
Felix Dobbs, CEO at Broken String Biosciences (搜索), emphasized the safety implications of the collaboration: "This collaboration with BioLizard (搜索) will set a new standard for safety in genome editing. By using AI to predict and distinguish natural and unnatural DNA (搜索) break patterns, human intervention is significantly reduced, while safety increases."
INDUCE-seq (搜索) is already in widespread use by drug developers to confirm on-target edits that drive efficacy and identify off-target edits that can become safety issues. The platform is available both as a lab-based service and through Broken String's recently announced Catalyst Early Access Program, which enables gene therapy developers to detect on- and off-target edits at high resolution within days in their own laboratories.
Accelerating Development Timelines
Liesbeth Ceelen, CEO at BioLizard (搜索), highlighted the potential impact on development efficiency: "We are bringing together our bioinformatics and AI expertise with Broken String's unique breakome identification capability to create SafeGuide (搜索). SafeGuide tool will significantly reduce the need for extensive off-target screening, ultimately accelerating gene editing development and reducing R&D costs."
The collaboration leverages BioLizard (搜索)'s expertise in scientific data analytics and AI, supported by a team of over 50 specialists with backgrounds spanning data management, software engineering, bioinformatics, advanced data analytics, and artificial intelligence. The company's proprietary Bio|Verse platform provides a foundation for developing bespoke software solutions tailored to specific client needs.
Industry Impact
The development of SafeGuide (搜索) addresses a fundamental challenge in the gene editing field, where the selection of optimal guide RNAs remains a time-intensive process requiring extensive laboratory validation. By providing AI-driven predictions of gRNA safety and efficacy profiles, the tool could substantially reduce the development timeline and costs associated with bringing gene-edited therapies to market.
The partnership represents a significant step toward more predictable and cost-effective gene editing development, potentially accelerating the availability of next-generation gene-edited therapies for patients while maintaining rigorous safety standards.